def interactive_hist(adata, keys=['n_counts', 'n_genes'],
bins='auto', max_bins=100,
groups=None, fill_alpha=0.4,
palette=None, display_all=True,
tools='pan, reset, wheel_zoom, save',
legend_loc='top_right',
plot_width=None, plot_height=None, save=None,
*args, **kwargs):
"""Utility function to plot distributions with variable number of bins.
Params
--------
adata: AnnData object
annotated data object
keys: list(str), optional (default: `['n_counts', 'n_genes']`)
keys in `adata.obs` or `adata.var` where the distibutions are stored
bins: int; str, optional (default: `auto`)
number of bins used for plotting or str from numpy.histogram
max_bins: int, optional (default: `1000`)
maximum number of bins possible
groups: list(str), (default: `None`)
keys in `adata.obs.obs_keys()`, groups by all possible combinations of values, e.g. for
3 plates and 2 time points, we would create total of 6 groups
fill_alpha: float[0.0, 1.0], (default: `0.4`)
alpha channel of the fill color
palette: list(str), optional (default: `None`)
palette to use
display_all: bool, optional (default: `True`)
display the statistics for all data
tools: str, optional (default: `'pan,reset, wheel_zoom, save'`)
palette of interactive tools for the user
legend_loc: str, (default: `'top_right'`)
position of the legend
legend_loc: str, default(`'top_left'`)
position of the legend
plot_width: int, optional (default: `None`)
width of the plot
plot_height: int, optional (default: `None`)
height of the plot
save: Union[os.PathLike, Str, NoneType], optional (default: `None`)
path where to save the plot
*args, **kwargs: arguments, keyword arguments
addition argument to bokeh.models.figure
Returns
--------
None
"""
if max_bins < 1:
raise ValueError(f'`max_bins` must >= 1')
palette = Set1[9] + Set2[8] + Set3[12] if palette is None else palette
# check the input
for key in keys:
if key not in adata.obs.keys() and \
key not in adata.var.keys() and \
key not in adata.var_names:
raise ValueError(f'The key `{key}` does not exist in `adata.obs`, `adata.var` or `adata.var_names`.')
def _create_adata_groups():
if groups is None:
return [adata], [('all',)]
combs = list(product(*[set(adata.obs[g]) for g in groups]))
adatas= [adata[reduce(lambda l, r: l & r,
(adata.obs[k] == v for k, v in zip(groups, vals)), True)]
for vals in combs] + [adata]
if display_all:
combs += [('all',)]
adatas += [adata]
return adatas, combs
# group_v_combs contains the value combinations
ad_gs = _create_adata_groups()
cols = []
for key in keys:
callbacks = []
fig = figure(*args, tools=tools, **kwargs)
slider = Slider(start=1, end=max_bins, value=0, step=1,
title='Bins')
plots = []
for j, (ad, group_vs) in enumerate(filter(lambda ad_g: ad_g[0].n_obs > 0, zip(*ad_gs))):
if key in ad.obs.keys():
orig = ad.obs[key]
hist, edges = np.histogram(orig, density=True, bins=bins)
elif key in ad.var.keys():
orig = ad.var[key]
hist, edges = np.histogram(orig, density=True, bins=bins)
else:
orig = ad[:, key].X
hist, edges = np.histogram(orig, density=True, bins=bins)
slider.value = len(hist)
# case when automatic bins
max_bins = max(max_bins, slider.value)
# original data, used for recalculation of histogram in JS code
orig = ColumnDataSource(data=dict(values=orig))
# data that we update in JS code
source = ColumnDataSource(data=dict(hist=hist, l_edges=edges[:-1], r_edges=edges[1:]))
legend = ', '.join(': '.join(map(str, gv)) for gv in zip(groups, group_vs)) \
if groups is not None else 'all'
p = fig.quad(source=source, top='hist', bottom=0,
left='l_edges', right='r_edges',
fill_color=palette[j], legend_label=legend if legend_loc is not None else None,
muted_alpha=0,
line_color="#555555", fill_alpha=fill_alpha)
# create callback and slider
callback = CustomJS(args=dict(source=source, orig=orig), code=_inter_hist_js_code)
callback.args['bins'] = slider
callbacks.append(callback)
# add the current plot so that we can set it
# visible/invisible in JS code
plots.append(p)
slider.end = max_bins
# slider now updates all values
slider.js_on_change('value', *callbacks)
button = Button(label='Toggle', button_type='primary')
button.callback = CustomJS(
args={'plots': plots},
code='''
for (var i = 0; i < plots.length; i++) {
plots[i].muted = !plots[i].muted;
}
'''
)
if legend_loc is not None:
fig.legend.location = legend_loc
fig.legend.click_policy = 'mute'
fig.xaxis.axis_label = key
fig.yaxis.axis_label = 'normalized frequency'
_set_plot_wh(fig, plot_width, plot_height)
cols.append(column(slider, button, fig))
if _bokeh_version > (1, 0, 4):
from bokeh.layouts import grid
plot = grid(children=cols, ncols=2)
else:
cols = list(map(list, np.array_split(cols, np.ceil(len(cols) / 2))))
plot = layout(children=cols, sizing_mode='fixed', ncols=2)
if save is not None:
save = save if str(save).endswith('.html') else str(save) + '.html'
bokeh_save(plot, save)
else:
show(plot)
def highlight_indices(adata, key, basis='diffmap', components=[1, 2], cell_keys='',
legend_loc='top_right', plot_width=None, plot_height=None,
tools='pan, reset, wheel_zoom, save'):
"""
Plot cell indices. Useful when trying to set adata.uns['iroot'].
Params
--------
adata: AnnData Object
annotated data object
key: str
key in `adata.obs_keys()` to color
basis: str, optional (default: `'diffmap'`)
basis to use
cell_keys: str, list(str), optional (default: `''`)
keys to display from `adata.obs_keys()` when hovering over cell
components: list[int], optional (default: `[1, 2]`)
which components of the basis to use
legend_loc: str, optional (default `'top_right'`)
location of the legend
tools: str, optional (default: `'pan, reset, wheel_zoom, save'`)
tools for the plot
plot_width: int, optional (default: `None`)
width of the plot
plot_width: int, optional (default: `None`)
height of the plot
Returns
--------
None
"""
if key not in adata.obs:
raise ValueError(f'{key} not found in `adata.obs`')
if f'X_{basis}' not in adata.obsm_keys():
raise ValueError(f'basis `X_{basis}` not found in `adata.obsm`')
if not isinstance(components, type(np.array)):
components = np.array(components)
if isinstance(cell_keys, str):
cell_keys = list(dict.fromkeys(map(str.strip, cell_keys.split(','))))
if cell_keys != ['']:
assert all(map(lambda k: k in adata.obs.keys(), cell_keys)), 'Not all keys are in `adata.obs.keys()`.'
else:
cell_keys = []
df = pd.DataFrame(adata.obsm[f'X_{basis}'][:, components - (basis != 'diffmap')], columns=['x', 'y'])
for k in cell_keys:
df[k] = list(map(str, adata.obs[k]))
df['index'] = range(len(df))
df[key] = list(adata.obs[key])
if hasattr(adata, 'obs_names'):
cell_keys.insert(0, 'name')
df['name'] = list(adata.obs_names)
if 'index' not in cell_keys:
cell_keys.insert(0, 'index')
palette = adata.uns.get(f'{key}_colors', viridis(len(df[key].unique())))
p = figure(title=f'{key}', tools=tools)
_set_plot_wh(p, plot_width, plot_height)
key_col = adata.obs[key].astype('category') if adata.obs[key].dtype.name != 'category' else adata.obs[key]
renderers = []
for c, color in zip(key_col.cat.categories, palette):
data = ColumnDataSource(df[df[key] == c])
renderers.append([p.scatter(x='x', y='y', size=10, color=color, source=data, muted_alpha=0)])
hover_cell = HoverTool(renderers=list(np.ravel(renderers)), tooltips=[(f'{k}', f'@{k}') for k in cell_keys])
if legend_loc is not None:
legend = Legend(items=list(zip(map(str, key_col.cat.categories), renderers)), location=legend_loc, click_policy='mute')
p.add_layout(legend)
p.legend.location = legend_loc
p.xaxis.axis_label = f'{basis}_{components[0]}'
p.yaxis.axis_label = f'{basis}_{components[1]}'
source = ColumnDataSource(df)
labels = LabelSet(x='x', y='y', text='index',
x_offset=4, y_offset=4,
level='glyph', source=source, render_mode='canvas')
labels.visible = False
p.add_tools(hover_cell)
p.add_layout(labels)
button = Button(label='Toggle Indices', button_type='primary')
button.callback = CustomJS(args=dict(l=labels), code='l.visible = !l.visible;')
show(column(button, p))